Method, apparatus, and system for blockchain consensus

ABSTRACT

A blockchain consensus method may comprise: acquiring transaction data; and distributing, according to a preset distribution rule, the transaction data to at least one consensus unit in a consensus unit set, causing the at least one consensus unit to perform consensus processing on the distributed transaction data.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/935,209 filed on Mar. 26, 2018, entitled “Method, Apparatus,and System for Blockchain Consensus,” which is based on and claimspriority to the Chinese Application No. 201710197538.X, filed Mar. 29,2017, each of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present application relates to the field of computer softwaretechnologies, and in particular, to methods, apparatuses, and systemsfor blockchain consensus.

BACKGROUND

Blockchain is a distributed database technology originally designed forBitcoin. Such data structure is particularly suitable for storingsequential data that is verified within the system. Moreover, such datastructure uses a consensus algorithm to ensure that the data cannot betampered with or forged. A consensus algorithm is an algorithm thatrequires the participation by nodes in a blockchain and reachesconsensus through joint calculation by a plurality of nodes. Forexample, upon receiving transaction data, a bookkeeping node broadcaststhe transaction data to other participating nodes, and otherparticipating nodes perform consensus processing to determine whetherthe bookkeeping node has a bookkeeping right for the transaction data.If the consensus result of other participating nodes is that thebookkeeping node has a bookkeeping right for the transaction data, thebookkeeping node stores the transaction data in a blockchaincorresponding to the bookkeeping node. In the blockchain technology,therefore, the consensus algorithm is a governing law for programs ornodes in a blockchain, which ensures that all nodes can cooperate in aconsistent manner in any circumstances.

Practical Byzantine Fault Tolerance (PBFT) is a common consensusalgorithm in blockchain. The PBFT algorithm can provide some tolerancewhile ensuring activity and security, and therefore has been extensivelyused. In the PBFT algorithm, one node is a master node, and the othernodes are backup nodes. The master node is responsible for sortingreceived transaction requests, and then broadcasting the transactionrequests to the backup nodes according to the sorting result. The PBFTalgorithm usually comprises three phases: pre-prepare, prepare, andcommit. The pre-prepare phase and the prepare phase are used todetermine a sequence of transaction requests.

However, it is likely that the master node commits errors when sortingtransaction requests, for example, giving the same sequence number todifferent transaction requests, or failing to assign a sequence number,or making sequence numbers of adjacent transaction requests to bediscontinuous, etc. As a result, it is necessary for a backup node toverify the sequence of transaction requests when receiving orderedtransaction requests.

As a result, the PBFT algorithm has done a lot of designs andcalculations to guarantee sequence. It has been found through researchthat many currently used consensus algorithms (e.g., consensusmechanisms like mechanism for proving work quantity and mechanism forproving rights and benefits, etc.) need to carry out a large amount ofdesigns and calculations on sequence when performing consensusprocessing, which consumes significant system resources.

In practical applications, however, there are many transaction requestswith no sequence requirements. A transaction request with no sequencerequirements means that, upon receiving transaction requests, a serverdoes not need to process the received transaction requests according toan order of accepting time, such as charity donation transactions,crowdfunding transactions with no upper limit or quota, etc. Withcharity donation transactions as an example, the sequence of donationdoes not have an impact on transaction processing. Therefore, this typeof transaction requests may be referred to as transaction requests thatdo not have sequence requirements (the “transaction request with nosequence requirements” in short). When this type of transaction requestsis processed in a blockchain, the use of a current consensus algorithmwill lead to a relatively low processing efficiency for this type oftransactions, and at the same time, also affect the consensus throughputof the blockchain.

SUMMARY

In view of the above, embodiments of the present disclosure providemethods, apparatuses, and systems for blockchain consensus to at leastmitigate the problem in the prior art that the processing efficiency islow when a consensus algorithm is used to process transaction requestswith no sequence requirements.

According to one aspect, a blockchain consensus method may comprise:acquiring transaction data; and distributing, according to a presetdistribution rule, the transaction data to at least one consensus unitin a consensus unit set, causing the at least one consensus unit toperform consensus processing on the distributed transaction data.

According to another aspect, a blockchain-based data storage method maycomprise: receiving consensus results from different consensus units;and storing the consensus results into blocks of the blockchainaccording to time stamps of the consensus results.

According to another aspect, a blockchain consensus system may comprise:a distribution unit and a consensus unit set comprising a plurality ofconsensus units, wherein: the distribution unit is configured to acquiretransaction data and distribute, according to a preset distributionrule, the transaction data to at least one consensus unit in theconsensus unit set; and the at least one consensus unit is configured toperform consensus processing on the distributed transaction data.

According to another aspect, a blockchain consensus apparatus,comprising a processor and a non-transitory computer-readable storagemedium storing instructions that, when executed by the processor, causethe block chain consensus apparatus to perform a method. The method maycomprise: acquiring transaction data; and distributing, according to apreset distribution rule, the transaction data to at least one consensusunit in a consensus unit set, causing the at least one consensus unit toperform consensus processing on the distributed transaction data.

According to another aspect, a blockchain based data storage apparatus,comprising a processor and a non-transitory computer-readable storagemedium storing instructions that, when executed by the processor, causethe block chain data storage apparatus to perform a method. The methodmay comprise: receiving consensus results from different consensusunits; and storing, according to time stamps of the consensus results,the consensus results into blocks of the blockchain, wherein the storingthe consensus results into blocks of the blockchain comprises: storingthe consensus results into blocks of nodes in the blockchain networkcorresponding to consensus units that generate the consensus results.

The present application discloses an exemplary consensus algorithm as anindependent consensus unit, which is different from a conventionalblockchain consensus, forms a consensus unit set with these consensusunits, and upon reception of transaction data, can distribute, accordingto a set distribution rule, the transaction data to consensus units inthe consensus unit set to achieve consensus processing on thetransaction data by the consensus units. In this way, a plurality ofconsensus units can perform consensus processing concurrently on atransaction request with no sequence requirements, the sequence handlingby existing consensus algorithms is simplified, the processingefficiency and processing throughput on transaction requests with nosequence requirements are improved, and the operating performance of ablockchain network is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

To more clearly describe technical solutions in the embodiments of thepresent disclosure, the accompanying drawings will be described brieflyas follows. The accompanying drawings are merely exemplary. To a personskilled in the art, other drawings may be further obtained according tothese drawings without inventive effort.

FIG. 1 is a flow chart of a blockchain consensus method according to anembodiment of the present disclosure.

FIG. 2 is a flow chart of a blockchain consensus method according to anembodiment of the present disclosure.

FIG. 3 is a flow chart of a blockchain consensus method according to anembodiment of the present disclosure.

FIG. 4 is a flow chart of a blockchain based data storage methodaccording to an embodiment of the present disclosure.

FIG. 5 is a structural schematic diagram of a blockchain consensussystem according to an embodiment of the present disclosure.

FIG. 6 is a schematic diagram of a blockchain consensus apparatusaccording to an embodiment of the present disclosure.

FIG. 7 is a schematic diagram of a blockchain consensus apparatusaccording to an embodiment of the present disclosure.

FIG. 8 is a structural schematic diagram of a blockchain based datastorage apparatus according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

To enable a person skilled in the art to better understand the technicalsolutions in the present disclosure, the technical solutions inembodiments will be clearly and completely described below withreference to the accompanying drawings. Apparently, the describedembodiments are merely exemplary. All other embodiments obtainable by aperson skilled in the art without inventive effort and on the basis ofthe embodiments of the present disclosure shall be encompassed by thescope of the present disclosure.

The solutions according to the present disclosure will be described indetail through the following embodiments.

FIG. 1 is a flow chart of a blockchain consensus method according to anembodiment of the present disclosure. From a perspective of programs, amain body that executes the flow may be an application (APP) or aprogram at a personal computer (PC) terminal. From a perspective ofapparatuses, a main body that executes the flow may include, but is notlimited to the following apparatuses: personal computers, large ormedium scale computers, computer clusters, cell phones, tabletcomputers, smart wearable devices, vehicular machines, etc.

According to one embodiment, the blockchain consensus method as shown inFIG. 1 may comprise the following steps:

S101: acquiring transaction data (e.g., transaction data that seeksconsensus processing in a blockchain network, and this transaction datacan be referred to as “to-be-consensused” transaction data).

In one embodiment, if a main body that executes the method is ablockchain node (hereinafter the “node”) in a blockchain network, thenode can receive a transaction request to be processed, acquiretransaction data from the transaction request, and store the transactiondata. The to-be-consensused transaction data in the step S101 may be thestored transaction data. The transaction request herein may be atransaction request sent by a client terminal, for example a bookkeepingrequest, or a request to execute another state machine operation; or maybe a transaction request sent by other apparatuses, which is not limitedherein.

In some embodiments, the to-be-consensused transaction data may refer toone set of transaction data, e.g., a subsequent operation is triggeredwhen one set of transaction data is received. Alternatively, theto-be-consensused transaction data may be a plurality sets ofto-be-consensused transaction data, e.g., a subsequent operation istriggered when a plurality sets of transaction data are received.Alternatively, the to-be-consensused transaction data may be transactiondata generated within a set time period, e.g., transaction datagenerated within a set time period is received, and when the set timeperiod expires, a subsequent operation is triggered. The number of setsof acquired transaction data is not limited herein.

The “transaction data” herein comprises transaction data with nosequence requirements. Namely, when acquired transaction data isprocessed, the time factor of the transaction data is not considered.For example, a received transaction request is a donation request. Then,the to-be-consensused transaction data is the acquired to-be-consensusedtransaction data. For transaction data related to donation, the sequenceof transaction data processing is irrelevant to the sender. Namely, thetime factor of when a donation request is sent by the sender is ignored.Therefore, this type of transaction request is also referred to as atransaction request with no sequence requirements.

S102: distributing, according to a preset distribution rule, thetransaction data to at least one consensus unit in the consensus unitset.

Here, the consensus unit is used to perform consensus processing on thedistributed transaction data.

In a blockchain technology, a consensus algorithm is used to process theacquired transaction data, while the consensus algorithm is an algorithmthat is completed by voting nodes participating in the consensus, e.g.,all the voting nodes ultimately reach a consensus result through jointcalculation.

The consensus unit set in one embodiment comprises consensus units, andeach consensus unit may provide independent consensus transaction. Inother words, different consensus units may use the same consensusalgorithm, or may use different consensus algorithms.

In some embodiments, the number of consensus units comprised in aconsensus unit set is not limited herein. The numbers of voting nodescomprised in different consensus units may be the same or may bedifferent. For example, if the consensus algorithm is the PBFTalgorithm, one voting node in a consensus unit comprises one primarynodes and four secondary nodes. If the consensus algorithm is the proofof work mechanism, a consensus unit comprises at least one voting node.Therefore, the number of voting nodes in a consensus unit is not limitedherein, which can be determined according to actual requirements of aconsensus algorithm.

In this way, with respect to to-be-consensused transaction data,consensus processing can be performed on different transaction data in aconcurrent manner according to the solutions of the various embodiments.Namely, different transaction data are concurrently distributed todifferent consensus units, and the consensus units independently performconsensus processing on the received transaction data.

Various methods for distributing, according to a preset distributionrule, the to-be-consensused transaction data to at least one consensusunit in the consensus unit set will be described in detail below.

In one example, a first distribution method may comprise: according tothe number of sets of the received to-be-consensused transaction data,determining, in a random manner, a matching number of consensus unitsfrom the consensus unit set; and distributing concurrently theto-be-consensused transaction data to the determined consensus units,respectively.

In one example, when the number of sets of the to-be-consensusedtransaction data is one, one consensus unit is determined, in a randommanner, from the consensus unit set. The to-be-consensused transactiondata is distributed to the determined consensus unit, and the consensusunit performs consensus processing on the to-be-consensused transactiondata.

When the number of sets of the to-be-consensused transaction data isgreater than one, according to the number of sets of theto-be-consensused transaction data, the same number of consensus unitsare determined, in a random manner, from the consensus unit set. Thesets of to-be-consensused transaction data are distributed sequentiallyto the determined different consensus units, respectively.

For example, the acquired to-be-consensused transaction data comprisesto-be-consensused transaction data 1, to-be-consensused transaction data2, and to-be-consensused transaction data 3, and assuming that aconsensus unit set comprises a consensus unit 1, a consensus unit 2, aconsensus unit 3, a consensus unit 4, and a consensus unit 5. Here,three consensus units can be randomly determined from the consensus unitset. Assuming that the randomly determined consensus units are theconsensus unit 2, the consensus unit 4, and the consensus unit 5, theto-be-consensused transaction data 1, to-be-consensused transaction data2, and to-be-consensused transaction data 3 are sequentiallydistributed, in a random manner, to the determined different consensusunits. For example, the to-be-consensused transaction data 1 isdistributed to the consensus unit 5; the to-be-consensused transactiondata 2 is distributed to the consensus unit 4; and the to-be-consensusedtransaction data 3 is distributed to the consensus unit 2.

The consensus distribution in an implementation mode of the presentapplication does not have any requirement for sequence. Therefore, arandom manner can be used for distribution. Further, the“to-be-consensused transaction data 1, to-be-consensused transactiondata 2, and to-be-consensused transaction data 3 are sequentiallydistributed, in a random manner, to the determined different consensusunits” may be implemented in a concurrently distributing manner. Thedistribution is carried out in a random manner. Namely, whento-be-consensused transaction data are acquired, any consensus unit canbecome a consensus unit of consensus transaction to process theto-be-consensused transaction data. Such a random distribution mannercan reduce resources used by the system in distribution.

The “random manner” herein may be implemented through a randomalgorithm, or may be implemented in other manners, which is notspecifically limited herein.

In another example, a second distribution method may comprise:determining, in a polling manner, consensus units in need of consensustransaction from the consensus unit set; and distributing theto-be-consensused transaction data to the determined consensus units.

In one example, when to-be-consensused transaction data is acquired, aninquiry message is sent, in a polling manner, to all consensus units inthe consensus unit set to determine whether there is a consensus unit inneed of a consensus transaction object (e.g., transaction request). Whenit is determined that a consensus unit is in need of a consensustransaction object, the to-be-consensused transaction data is sent tothe consensus unit.

When the number of the to-be-consensused transaction data is one, aninquiry message is sent sequentially to all consensus units in theconsensus unit set. When a received response message indicates that aconsensus transaction object is needed, the to-be-consensusedtransaction data is sent to the consensus unit that returns the responsemessage.

When the number of the to-be-consensused transaction data is greaterthan one, an inquiry message may be sent concurrently to all consensusunits in the consensus unit set. When a response message is received,the number of consensus units in need of a consensus transaction object,as indicated in the received response message, is determined. If thenumber of consensus units is greater than the number of transactionrequests, consensus units can be selected randomly according to thefirst manner from the consensus units in need of a consensus transactionobject as indicated in the received response message, and theto-be-consensused transaction data is sent concurrently to the randomlyselected consensus units, respectively. If the number of consensus unitsis not greater than the number of transaction requests, theto-be-consensused transaction data is randomly distributed to theconsensus units in need of a consensus transaction object as indicatedin the received response message, and the to-be-consensused transactiondata that has not been distributed will continue to wait.

For example, it is determined, in a polling manner, that there are threeconsensus units in need of a consensus transaction object. Assume thatthere are four sets of acquired to-be-consensused transaction data.Then, the three sets of to-be-consensused transaction data are randomlyselected from the four sets of acquired to-be-consensused transactiondata. The three sets of randomly selected to-be-consensused transactiondata are sent to the consensus units in need of a consensus transactionobject, respectively, and the one set of to-be-consensused transactiondata that has not been distributed is in a to-be-distributed state.

Assuming that there are two sets of acquired to-be-consensusedtransaction data, the two sets of to-be-consensused transaction data arerandomly selected from the to-be-consensused transaction data determinedto be in need of a consensus transaction object. The two sets ofacquired to-be-consensused transaction data are concurrently sent to therandomly selected consensus units, such that the consensus units performconsensus processing on the received to-be-consensused transaction data.

In another example, a third distribution method may comprise:determining a load capacity of each consensus unit in the consensus unitset; and distributing, according to a load balancing rule, theto-be-consensused transaction data to at least one consensus unit in theconsensus unit set.

In one example, when to-be-consensused transaction data are acquired,the load capacity (the load capacity here may refer to the load statusof the current consensus unit, which may be different from the totalload capacity of the consensus unit) of each consensus unit in theconsensus unit set can be determined to ensure that the loading isbalanced for all consensus units in the consensus unit set. Furthermore,the current quantity of idle resources of each consensus unit can bedetermined according to the load capacity of each consensus unit. In oneembodiment, to-be-consensused transaction data can be distributed toconsensus units in the consensus unit set that have a load capacitybelow a set condition. Namely, to-be-consensused transaction data aredistributed to consensus units in the consensus unit set that have idleresources greater than a set threshold.

In some embodiments, with respect to the situation in which the numberof consensus units in need of a consensus transaction object is greaterthan the number of transaction requests in the above second distributionmethod, the load balancing method according to the third distributionmethod, in addition to the random method, may be used to determineconsensus units, which will not be described in detail herein.

In one embodiment, when the acquired to-be-consensused transaction datacannot be all distributed to consensus units in one time, a part of thetransaction requests need to be in a to-be-distributed state. Then, forthe transaction requests in a to-be-distributed state, consensus unitscan still be determined by using the above three distribution methods.

For example, a consensus unit set comprises three consensus units. Then,the consensus unit set can concurrently process three to-be-consensusedtransaction data acquired at the same time point. If four (i.e., greaterthan three) sets of to-be-consensused transaction data are acquired: theto-be-consensused transaction data 1, to-be-consensused transaction data2, to-be-consensused transaction data 3, and to-be-consensusedtransaction data 4. Then, it is assumed that the to-be-consensusedtransaction data 1, to-be-consensused transaction data 2, andto-be-consensused transaction data 3 are concurrently distributed to thethree consensus units in the consensus unit set, and theto-be-consensused transaction data 4 will be in a to-be-distributedstate. Once it is detected that a consensus unit in the consensus unitset becomes idle, the to-be-consensused transaction data 4 can bedistributed to the consensus unit.

In one embodiment, the determining consensus units from a consensus unitset comprises: determining a working state of each consensus unit in theconsensus unit set, the working state comprising at least one of normalstate and abnormal state; and determining consensus units from consensusunits with the working state being a normal state.

To ensure that a determined consensus unit can successfully performconsensus processing on acquired to-be-consensused transaction data, theworking state of consensus units in a consensus unit set can be furthermonitored. In such a way, when consensus units are selected, theconsensus units with an abnormal working state can be avoided, whicheffectively improve the processing efficiency of transaction requests.

In addition, consensus units in a consensus unit set can be furtherprovided with a switch control. In such a way, when the quantity oftransaction requests to be processed in a blockchain network isrelatively small, some consensus units in a consensus unit set can beturned off, which saves system resources to improve the utilization rateof the system resources. In some embodiments, by controlling the switchstate of each consensus unit, the blockchain in the implementationmanner of the present application can improve the system availabilitywhen facing issues like downtime or Internet disconnection. For example,when node downtime or Internet disconnection occurs to a consensus unit,the drawback of low system availability in an existing manner can beavoided by shutting down the consensus unit.

S103: the consensus unit performs (e.g., is caused to perform) consensusprocessing on the distributed transaction data.

In one embodiment, the consensus algorithm used by consensus units isnot limited herein, which can be any consensus algorithm, including themainstream PBFT algorithm.

A person skilled in the art should understand that there is no sequencerequirement in implementation manners of the present disclosure fordistributing the to-be-consensused transaction data. In this disclosure,a consensus algorithm can be designed as an independent consensus unit,different from a conventional blockchain consensus. A consensus unit setis formed by these consensus units, and upon reception ofto-be-consensused transaction data, the transaction data can bedistributed, according to a set distribution rule, to consensus units inthe consensus unit set to achieve consensus processing on thetransaction data by the consensus units. In this way, a plurality ofconsensus units can perform consensus processing concurrently on atransaction request with no sequence requirements, the sequence handlingby existing consensus algorithms is simplified, the processingefficiency and processing throughput on transaction requests with nosequence requirements are improved, and the operating performance of ablockchain network is improved.

In some embodiments, the transaction requests may comprise transactionrequests with no sequence requirements. The transaction request with nosequence requirements include, but are not limited to, charity donationtransactions, crowdfunding transactions with no upper limit or quota,etc. With respect to a charity donation transaction, the sequence ofdonation may not have an impact on the transaction at a millisecondlevel, and crowdfunding transactions with no upper limit or quota mayhave the same nature. Therefore, this type of transaction requests canbe treated as requests with no sequence requirements. In one embodiment,the operating efficiency of consensus processing can be improved in aconcurrent consensus manner for this type of transaction requests withno sequence requirements. With the adoption of such a concurrentconsensus manner, the throughput of the entire blockchain system isgreatly improved, and in particular, a consensus algorithm module is nolonger a bottleneck in the entire blockchain system.

FIG. 2 is a flow chart of a blockchain consensus method according to anembodiment of the present disclosure. The method may include thefollowing steps.

S201: acquiring transaction data.

This is identical with or similar to the manner of the step S101 withreference to FIG. 1, which will not be described in detail.

S202: determining a quantity of resources required to process thetransaction data and a quantity of idle resources of each consensus unitin the consensus unit set.

In one embodiment, when to-be-consensused transaction data are acquired,resources required to process the to-be-consensused transaction data canbe determined.

Furthermore, a current quantity of idle resources of each consensus unitin the consensus unit set can be determined through inquiry.

S203: comparing the quantity of idle resources of each consensus unitwith the resources required to process the transaction data,respectively, and determining the number of consensus units having aquantity of idle resources greater than the quantity of resourcesrequired to process the transaction data.

S204: determining if the number is greater than a set value; if yes, themethod proceeds to S205; otherwise, the method proceeds to S206.

The set value in the embodiments of the present disclosure may bedetermined as needed, or may be determined according to experimentaldata, which is not limited herein.

S205: determining, in a polling manner, consensus units in need ofconsensus transaction from the consensus units having more idleresources than the resources required to process the transaction data,and distributing the transaction data to the determined consensus units.

The second distribution method described above with reference to thestep S102 may be used here as the polling manner to determine consensusunits in need of consensus transaction.

In one embodiment, a mechanism of polling and distributing eachto-be-consensused transaction data is adopted. According to the pollingand distributing mechanism, an adaptor or a distributing unit in chargeof distribution periodically sends out a poll to sequentially poll eachconsensus unit whether a consensus transaction object is needed. If yes,a transaction is provided, i.e., to-be-consensused transaction data isdistributed, and when the transaction is completed, the next consensusunit is polled, which is continuously repeated.

S206: determining, according to a load balancing rule, consensus unitshaving a load capacity below a set condition from the consensus unitshaving more idle resources than the resources required to process thetransaction data, and distributing the transaction data to thedetermined consensus units.

The third distribution method described above with reference to the stepS102 may be used as the manner here in which a load balancing rule isused to determine consensus units.

For example, there are five sets of to-be-consensused transaction datawaiting for processing at a time point, while there are currently threeconsensus units capable of participating in the consensus transaction.Then, the loading of the transaction requests exceeds the processingcapability of the independent consensus units. The loading ofto-be-consensused transaction data and the processing capability of theindependent consensus units are compared to provide reference for thenext consensus distribution, such that each consensus unit is used toits maximum capacity and the consensus processing quantity per unittime, e.g., throughput, is improved.

In some embodiments, the load balancing distribution method usedincludes, but is not limited to, a software or hardware load balancingmanner, for example, through DNS load balancing, gateway load balancing,or a load balancer. The distribution in a load balancing manner canprevent a lot of transaction requests from overloading some individualconsensus units, while leaving other individual consensus units idled.

Compared with the distribution in a load balancing method, the pollingdistribution method is easier to operate, but the distribution in a loadbalancing method has higher efficiency.

FIG. 3 is a flow chart of a blockchain consensus method according to anembodiment of the present disclosure. The method may further be asfollows.

S301: acquiring at least one set of transaction data.

S302: grouping the at least one transaction data to obtain one or moretransaction data groups.

In one embodiment, as the to-be-consensused transaction data aretransaction data with no sequence requirements, the acquiredto-be-consensused transaction data can be grouped, such that theprocessing efficiency can be improved for the transaction data.

S303: according to the preset distribution rule, distributing (e.g.,concurrently distributing) the transaction data groups to differentconsensus units in the consensus unit set.

Here, the consensus unit is used to perform consensus processing on thedistributed to-be-consensused transaction data.

FIG. 4 is a flow chart of a blockchain based data storage methodaccording to an embodiment of the present disclosure. The method may beas follows.

S401: receiving consensus results sent by different consensus units in ablockchain network.

In one embodiment, consensus units may have a data storage capability.When a consensus result is obtained, the consensus result just needs tobe stored; they may also not have a data storage capability, thendifferent consensus units may send the obtained consensus results to apublic node, and the public node will complete storage.

S402: storing the consensus results into blocks of the blockchainnetwork according to time stamps of the consensus results.

In one example, according to the generation time of the consensusresults, the different consensus results are sequentially stored intoblocks of the blockchain; alternatively, according to the reception timeof the consensus results, the different consensus results aresequentially stored into blocks of the blockchain.

In one embodiment, when the consensus results are stored into blocks ofthe blockchain, storage nodes may be selected in a random manner; oraccording to a correspondence relationship between consensus units andstorage nodes, the consensus results are stored into blocks of nodes inthe blockchain network corresponding to consensus units that generatethe consensus results, which is not limited herein. Namely, a consensusunit can store consensus results generated by itself, store consensusresults generated by other consensus units, or does not store consensusresults with the consensus results stored to independent storage nodes.

In one example, in addition to the provision of switch control for eachconsensus unit, a switch control may be further provided for a storagenode. When facing the downtime of storage nodes, Internet disconnection,addition of new nodes and push-out of old nodes, such a switch controlgreatly improves the availability of the entire blockchain system.

FIG. 5 is a structural schematic diagram of a blockchain consensussystem according to an embodiment of the present disclosure. Theconsensus system comprises: a distribution unit 501 and a consensus unitset 502 comprising consensus units 5021 a-d, wherein: the distributionunit 501 acquires to-be-consensused transaction data and distributes,according to a preset distribution rule, the to-be-consensusedtransaction data to at least one consensus unit (e.g., 5021 a, b, c, ord) in the consensus unit set 502; and the consensus units 5021 a-dcomprised in the consensus unit set 502 perform consensus processing onthe distributed to-be-consensused transaction data.

In one embodiment, the consensus system further comprises one or morestorage nodes 503 a-d, wherein: the storage nodes 503 a-d receivesconsensus results sent by different consensus units in a blockchainnetwork and sequentially stores, according to time stamps of theconsensus results, the consensus results.

In one embodiment, the distribution unit 501 distributing, according toa preset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises: accordingto the number of sets of the received to-be-consensused transactiondata, determining, in a random manner, a matching number of consensusunits from the consensus unit set; and distributing concurrently theto-be-consensused transaction data to the determined consensus units,respectively.

In one embodiment, the distribution unit 501 distributing, according toa preset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:determining, in a polling manner, consensus units in need of consensustransaction from the consensus unit set; and distributing theto-be-consensused transaction data to the determined consensus units.

In one embodiment, the distribution unit 501 distributing, according toa preset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:determining a load capacity of each consensus unit in the consensus unitset; and distributing, according to a load balancing rule, theto-be-consensused transaction data to at least one consensus unit in theconsensus unit set.

In one embodiment, the distribution unit 501 distributing, according toa load balancing rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:distributing the to-be-consensused transaction data to consensus unitsin the consensus unit set that have a load capacity below a setcondition.

In one embodiment, the distribution unit 501 distributing, according toa preset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:determining a quantity of resources required to process theto-be-consensused transaction data and a quantity of idle resources ofeach consensus unit in the consensus unit set; when the number ofconsensus units having a quantity of idle resources greater than thequantity of resources required to process the to-be-consensusedtransaction data is greater than a set value, determining, in a pollingmanner, consensus units in need of consensus transaction from theconsensus units having more idle resources than the resources requiredto process the to-be-consensused transaction data, and distributing theto-be-consensused transaction data to the determined consensus units;and when the number of consensus units having a quantity of idleresources greater than the quantity of resources required to process theto-be-consensused transaction data is smaller than a set value,determining, according to a load balancing rule, consensus units havinga load capacity below a set condition from the consensus units havingmore idle resources than the resources required to process theto-be-consensused transaction data, and distributing theto-be-consensused transaction data to the determined consensus units.

In one embodiment, the distribution unit 501 determining consensus unitsfrom the consensus unit set comprises: determining a working state ofeach consensus unit in the consensus unit set, the working statecomprising at least one of normal state and abnormal state; anddetermining consensus units from consensus units with the working statebeing a normal state.

In one embodiment, the to-be-consensused transaction data comprisestransaction data with no sequence requirements.

In one embodiment, the storage node 503 receives consensus results sentby different consensus units in a blockchain network; and stores,according to time stamps of the consensus results, the consensus resultsinto blocks of the blockchain network.

In one embodiment, the storage node 503 storing the consensus resultsinto blocks of the blockchain comprises: storing the consensus resultsinto blocks of nodes in the blockchain network corresponding toconsensus units that generate the consensus results.

FIG. 6 is a schematic diagram of a blockchain consensus apparatus 600according to an embodiment of the present disclosure. The consensusapparatus 600 may comprise an acquiring unit 601 and a processing unit602, wherein: the acquiring unit 601 acquires to-be-consensusedtransaction data; and the processing unit 602 distributes, according toa preset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set, and the consensusunit is configured to perform consensus processing on the distributedto-be-consensused transaction data. In some embodiments, the blockchainconsensus apparatus 600 may comprise a memory and a processor coupledtogether. The memory may be non-transitory and computer-readable and maystore instructions that, when executed by the processor, cause theapparatus 600 to perform one or more steps described herein. Theinstructions may be implemented as various units as described herein.Each unit may be a software, a hardware, or a combination of both.

In one embodiment, the processing unit 602 distributing, according to apreset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises: accordingto the number of sets of the received to-be-consensused transactiondata, determining, in a random manner, a matching number of consensusunits from the consensus unit set; and distributing concurrently theto-be-consensused transaction data to the determined consensus units,respectively.

In one embodiment, the processing unit 602 distributing, according to apreset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:determining, in a polling manner, consensus units in need of consensustransaction from the consensus unit set; and distributing theto-be-consensused transaction data to the determined consensus units.

In one embodiment, the processing unit 602 distributing, according to apreset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:determining a load capacity of each consensus unit in the consensus unitset; and distributing, according to a load balancing rule, theto-be-consensused transaction data to at least one consensus unit in theconsensus unit set.

In one embodiment, the processing unit 602 distributing, according to aload balancing rule, the to-be-consensused transaction data to at leastone consensus unit in the consensus unit set comprises: distributing theto-be-consensused transaction data to consensus units in the consensusunit set that have a load capacity below a set condition.

In one embodiment, the processing unit 602 distributing, according to apreset distribution rule, the to-be-consensused transaction data to atleast one consensus unit in the consensus unit set comprises:determining a quantity of resources required to process theto-be-consensused transaction data and a quantity of idle resources ofeach consensus unit in the consensus unit set; when the number ofconsensus units having a quantity of idle resources greater than thequantity of resources required to process the to-be-consensusedtransaction data is greater than a set value, determining, in a pollingmanner, consensus units in need of consensus transaction from theconsensus units having more idle resources than the resources requiredto process the to-be-consensused transaction data, and distributing theto-be-consensused transaction data to the determined consensus units;and when the number of consensus units having a quantity of idleresources greater than the quantity of resources required to process theto-be-consensused transaction data is smaller than a set value,determining, according to a load balancing rule, consensus units havinga load capacity below a set condition from the consensus units havingmore idle resources than the resources required to process theto-be-consensused transaction data, and distributing theto-be-consensused transaction data to the determined consensus units.

In one embodiment, the processing unit 602 determining consensus unitsfrom the consensus unit set comprises: determining a working state ofeach consensus unit in the consensus unit set, the working statecomprising at least one of normal state and abnormal state; anddetermining consensus units from consensus units with the working statebeing a normal state.

In one embodiment, the to-be-consensused transaction data comprisestransaction data with no sequence requirements.

The consensus apparatus according to the various embodiments may beimplemented in a software manner, or may be implemented in a hardwaremanner, which is not limited herein. In other words, the units 601 and602 can be software functional units stored in a memory. When thesoftware functional units are executed by a processor, they cause theprocessor to perform the described functions. The units 601 and 602 mayalso be hardware units, such as, programmed circuitry for performing thedescribed functions.

FIG. 7 is a schematic diagram of a blockchain consensus apparatus 700according to an embodiment of the present application. The consensusapparatus 700 may comprise an acquiring unit 701 and a processing unit702, wherein: the acquiring unit acquires at least one to-be-consensusedtransaction data; and the processing unit concurrently distributes,according to a preset distribution rule, the to-be-consensusedtransaction data to at least one consensus unit in the consensus unitset, and the consensus unit is configured to perform consensusprocessing on the distributed to-be-consensused transaction data. Insome embodiments, the blockchain consensus apparatus 700 may comprise amemory and a processor coupled together. The memory may benon-transitory and computer-readable and may store instructions that,when executed by the processor, cause the apparatus 700 to perform oneor more steps described herein. The instructions may be implemented asvarious units as described herein. Each unit may be a software, ahardware, or a combination of both. For example, the units 701 and 702can be software functional units stored in a memory. When the softwarefunctional units are executed by a processor, they cause the processorto perform the described functions. The units 701 and 702 may also behardware units, such as, programmed circuitry for performing thedescribed functions.

In one embodiment, the processing unit 702 concurrently distributing,according to a preset distribution rule, the to-be-consensusedtransaction data to at least one consensus unit in the consensus unitset comprises: grouping the at least one to-be-consensused transactiondata to obtain one or more transaction data groups; and according to thepreset distribution rule, distributing the transaction data groups todifferent consensus units in the consensus unit set.

FIG. 8 is a structural schematic diagram of a blockchain based datastorage apparatus 800 according to an embodiment of the presentapplication. The storage apparatus 800 may comprise a receiving unit 801and a storing unit 802, wherein: the receiving unit 801 receivesconsensus results sent by different consensus units in a blockchainnetwork; and the storing unit 802 stores, according to time stamps ofthe consensus results, the consensus results into blocks of theblockchain network. In some embodiments, the blockchain consensusapparatus 800 may comprise a memory and a processor coupled together.The memory may be non-transitory and computer-readable and may storeinstructions that, when executed by the processor, cause the apparatus800 to perform one or more steps described herein. The instructions maybe implemented as various units as described herein. Each unit may be asoftware, a hardware, or a combination of both. For example, the units801 and 802 can be software functional units stored in a memory. Whenthe software functional units are executed by a processor, they causethe processor to perform the described functions. The units 801 and 802may also be hardware units, such as, programmed circuitry for performingthe described functions.

In one embodiment, the storing unit 802 storing the consensus resultsinto blocks of the blockchain comprises: storing the consensus resultsinto blocks of nodes in the blockchain network corresponding toconsensus units that generate the consensus results.

The consensus apparatus according to various embodiments may beimplemented in a software manner, or may be implemented in a hardwaremanner, which is not limited herein. With respect to the consensusapparatus, a consensus algorithm is designed as an independent consensusunit, which is different from a conventional blockchain consensus. Aconsensus unit set is formed by these consensus units, and uponreception of to-be-consensused transaction data, the transaction datacan be distributed, according to a set distribution rule, to consensusunits in the consensus unit set to achieve consensus processing on thetransaction data by the consensus units. In this way, a plurality ofconsensus units can perform consensus processing concurrently on atransaction request with no sequence requirements, the sequence handlingby existing consensus algorithms is simplified, the processingefficiency and processing throughput on transaction requests with nosequence requirements are improved, and the operating performance of ablockchain network is improved.

According to the embodiments of the present disclosure, the apparatusesrespectively correspond to the methods. Therefore, the apparatuses alsoachieve advantageous technical effects that are similar to those of thecorresponding methods. As the advantageous technical effects of themethods have been described in detail above, the advantageous technicaleffects of the corresponding apparatuses will not be repeated herein.

In the 1990s, an improvement to a technology could be differentiatedinto a hardware improvement (e.g. an improvement to a circuit structure,such as a diode, a transistor, a switch, and the like) or a softwareimprovement (an improvement to a flow of a method). Along with thetechnological development, however, many current improvements to methodflows can be deemed as direct improvements to hardware circuitstructures. Designers can obtain a corresponding hardware circuitstructure by programming an improved method flow into a hardwarecircuit. Therefore, an improvement to a method flow can be realized byhardware implementation. For example, Programmable Logic Device (PLD)(e.g., Field Programmable Gate Array (FPGA)) is such an integratedcircuit that its logic functions are determined by a user throughprogramming the device. A designer can program to “integrate” a digitalsystem onto one piece of PLD, without asking a chip manufacturer todesign and manufacture a dedicated IC chip. At present, this type ofprogramming has mostly been implemented through “logic compiler”software, rather than manually manufacturing the IC chips. The logiccompiler software is similar to a software compiler used for programdevelopment and writing, while a particular programming language is usedfor writing source codes prior to compiling, which is referred to as aHardware Description Language (HDL). There is not just one, but manytypes of HDL, such as ABEL (Advanced Boolean Expression Language), AHDL(Altera Hardware Description Language), Confluence, CUPL (CornellUniversity Programming Language), HDCal, JHDL (Java Hardware DescriptionLanguage), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware DescriptionLanguage). The most commonly used HDL includes VHDL (Very-High-SpeedIntegrated Circuit Hardware Description Language) and Verilog. A personskilled in the art would have known obtaining a hardware circuit toimplement a logic method flow by using the above HDLs to perform somelogic programming on the method flow and program it into an IC.

A controller may be implemented in any proper manner. For example, acontroller may be in, for example, a form of a microprocessor orprocessor, as well as a computer readable medium that stores computerreadable program codes (e.g. software or firmware) capable of beingexecuted by the (micro) processor, a logic gate, a switch, anApplication Specific Integrated Circuit (ASIC), a programmable logiccontroller and an embedded microcontroller. Examples of the controllerinclude, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320.A memory controller may further be implemented as a part of a controllogic of a memory. A person skilled in the art should also be awarethat, in addition to that a controller is implemented in a manner ofpure computer readable program codes, it is feasible to perform logicprogramming on steps of a method to enable a controller to implement thesame functions in a form of a logic gate, a switch, an ASIC, aprogrammable logic controller and an embedded microcontroller.Therefore, such a controller can be deemed as a hardware part, whiledevices comprised therein and configured to carry out various functionsmay also be deemed as a structure inside the hardware part.Alternatively, devices configured to carry out various functions may bedeemed as both software modules to implement a method and a structureinside a hardware part.

The system, apparatus, module or unit described in the above embodimentsmay be implemented by a computer chip or entity or implemented by aproduct having a function. A typical implementation device is acomputer. For example, a computer may be a personal computer, a laptopcomputer, a cellular phone, a camera phone, a smart phone, a personaldigital assistant, a medium player, a navigation device, an emailapparatus, a game console, a tablet computer, a wearable device or acombination of any devices in these devices.

For convenience of description, the above apparatus is divided intovarious units according to functions for description. Functions of theunits may be implemented in one or multiple pieces of software and/orhardware when implementing the present disclosure.

A person skilled in the art should understand that the embodiments ofthe present disclosure may be provided as a method, a system, or acomputer program product. Therefore, the disclosed system may beimplemented as a complete hardware embodiment, a complete softwareembodiment, or an embodiment combing software and hardware forperforming the disclosed methods. Moreover, the disclosed system may bein the form of a computer program product implemented on one or morecomputer usable storage media (including, but not limited to, a magneticdisk memory, CD-ROM, an optical memory, and the like) comprisingcomputer usable program codes therein.

The disclosed system is described with reference to flowcharts and/orblock diagrams of the method, device (system) and computer programproduct according to the embodiments of the present disclosure. Acomputer program instruction may be used to implement each processand/or block in the flowcharts and/or block diagrams and a combinationof processes and/or blocks in the flowcharts and/or block diagrams.These computer program instructions may be provided for ageneral-purpose computer, a special-purpose computer, an embeddedprocessor, or a processor of other programmable data processing devicesto generate a machine, so that the instructions executed by a computeror a processor of other programmable data processing devices generate anapparatus for implementing a specified function in one or more processesin the flowcharts and/or in one or more blocks in the block diagrams.

These computer program instructions may also be stored in a computerreadable memory that can instruct a computer or other programmable dataprocessing devices to work in a particular manner, such that theinstructions stored in the computer readable memory generate amanufactured article that includes an instruction apparatus. Theinstruction apparatus implements one or more functions in one or moreprocesses in the flowcharts and/or in one or more blocks in the blockdiagrams.

These computer program instructions may also be loaded onto a computeror other programmable data processing devices, such that a series ofoperational steps are performed on the computer or other programmabledevices, thereby generating computer-implemented processing. Therefore,the instructions executed on the computer or other programmable devicesprovide steps for implementing one or more functions in one or moreprocesses in the flowcharts and/or in one or more blocks in the blockdiagrams.

In a typical configuration, the computation device includes one or moreCentral Processing Units (CPUs), input/output interfaces, networkinterfaces, and a memory.

The memory may include computer readable media, such as a volatilememory, a Random Access Memory (RAM), and/or a non-volatile memory,e.g., a Read-Only Memory (ROM) or a flash RAM. The memory is an exampleof a computer readable medium.

Computer readable media include permanent, volatile, mobile and immobilemedia, which can implement information storage through any method ortechnology. The information may be computer readable instructions, datastructures, program modules or other data. Examples of storage media ofcomputers include, but are not limited to, Phase-change RAMs (PRAMs),Static RAMs (SRAMs), Dynamic RAMs (DRAMs), other types of Random AccessMemories (RAMs), Read-Only Memories (ROMs), Electrically ErasableProgrammable Read-Only Memories (EEPROMs), flash memories or othermemory technologies, Compact Disk Read-Only Memories (CD-ROMs), DigitalVersatile Discs (DVDs) or other optical memories, cassettes, cassetteand disk memories or other magnetic memory devices or any othernon-transmission media, which can be used for storing informationaccessible to a computation device. According to the definitions herein,the computer readable media do not include transitory media, such asmodulated data signals and carriers.

The terms of “including”, “comprising” or any other variants thereofintend to encompass a non-exclusive inclusion, such that a process,method, commodity or device comprising a series of elements not onlycomprises these elements, but also comprises other elements that are notlisted, or further comprises elements that are inherent to the process,method, commodity or device. When there is no further restriction,elements defined by the statement “comprising one . . . ” does notexclude additional similar elements in a process, method, commodity ordevice that comprises the defined elements.

The present disclosure may be described in a regular context of acomputer executable instruction that is executed by a computer, such asa program module. In various embodiments, the program module comprises aroutine, a program, an object, a component, a data structure, and thelike for executing a particular task or implementing a particularabstract data type. The present disclosure may also be practiced indistributed computing environments. In these distributed computingenvironments, remote processing devices connected via communicationnetworks carry out tasks. In the distributed computing environments, aprogram module can be located in local and remote computer storagemedia, including storage devices.

The embodiments in this description are described in a progressivemanner with each embodiment focusing on differences from otherembodiments, and the embodiments may be mutually referenced foridentical or similar parts thereof. For the system embodiment, thedescription thereof is relatively simple as it is substantially similarto the method embodiment. The description of the method embodiment maybe referenced for related parts thereof.

The embodiments of the present disclosure are merely exemplary, and arenot used to limit the present disclosure. To a person skilled in theart, the disclosed embodiments can be modified or changed in variousways. Any modification, equivalent substitution or improvement madewithin the spirit and principle of the present disclosure shall beencompassed by the claims of the present disclosure.

The invention claimed is:
 1. A method, comprising: acquiring transactiondata from multiple transaction requests with no sequence requirements,the acquired transaction data comprising first transaction data of afirst transaction request of the transaction requests and secondtransaction data of a second transaction request of the transactionrequests, wherein each of the transaction requests with no sequencerequirements does not require processing according to a time factor ofwhen the transaction request was sent by a sender relative to othertransaction requests acquired for consensus processing; determining aquantity of resources required to process the acquired transaction data;determining a number of consensus units in a blockchain network thathave a quantity of idle resources that is no less than the quantity ofresources required to process the acquired transaction data;determining, based on the number, at least two consensus units from theconsensus units by: if the number is greater than a threshold, pollingthe consensus units to determine which of the consensus units is in needof a consensus transaction; and if the number is less than or equal tothe threshold, determining which of the consensus units has a loadcapacity below a set condition; and distributing the first transactiondata to a first consensus unit of the at least two consensus units andthe second transaction data to a second consensus unit of the at leasttwo consensus units without considering the time factor, causing the atleast two consensus units to perform consensus processing on the firsttransaction data and the second transaction data without considering thetime factor.
 2. The method of claim 1, wherein after the firsttransaction data and the second transaction data are distributed, thefirst consensus unit and the second consensus unit concurrently performconsensus processing on the first transaction data and the secondtransaction data, respectively.
 3. The method of claim 1, whereindetermining the at least two consensus units, comprises: determiningthat the number is greater than the threshold, and polling the consensusunits to determine which of the consensus units is in need of theconsensus transaction.
 4. The method of claim 3, wherein polling theconsensus units comprises: sequentially polling each of the consensusunits to determine if it is in need of the consensus transaction.
 5. Themethod of claim 1, wherein determining the at least two consensus units,comprises: determining that the number is less than or equal to thethreshold, and selecting each of the at least two consensus units basedon the determined load capacity of the consensus unit.
 6. The method ofclaim 1, wherein the multiple transaction requests comprise a charitydonation transaction or a crowdfunding transaction with no upper limitor quota.
 7. A non-transitory computer-readable storage medium storinginstructions that, when executed by a processor, cause the processor toperform a method comprising: acquiring transaction data from multipletransaction requests with no sequence requirements, the acquiredtransaction data comprising first transaction data of a firsttransaction request of the transaction requests and second transactiondata of a second transaction request of the transaction requests,wherein each of the transaction requests with no sequence requirementsdoes not require processing according to a time factor of when thetransaction request was sent by a sender relative to other transactionrequests acquired for consensus processing; determining a quantity ofresources required to process the acquired transaction data; determininga number of consensus units in a blockchain network that have a quantityof idle resources that is no less than the quantity of resourcesrequired to process the acquired transaction data; determining, based onthe number, at least two consensus units from the consensus units by: ifthe number is greater than a threshold, polling the consensus units todetermine which of the consensus units is in need of a consensustransaction; and if the number is less than or equal to the threshold,determining which of the consensus units has a load capacity below a setcondition; and distributing the first transaction data to a firstconsensus unit of the at least two consensus units and the secondtransaction data to a second consensus unit of the at least twoconsensus units without considering the time factor, causing the atleast two consensus units to perform consensus processing on the firsttransaction data and the second transaction data without considering thetime factor.
 8. The non-transitory computer-readable storage medium ofclaim 7, wherein after the first transaction data and the secondtransaction data are distributed, the first consensus unit and thesecond consensus unit concurrently perform consensus processing on thefirst transaction data and the second transaction data, respectively. 9.The non-transitory computer-readable storage medium of claim 7, whereindetermining the at least two consensus units, comprises: determiningthat the number is greater than the threshold, and polling the consensusunits to determine which of the consensus units is in need of theconsensus transaction.
 10. The non-transitory computer-readable storagemedium of claim 9, wherein polling the consensus units comprises:sequentially polling each of the consensus units to determine if it isin need of the consensus transaction.
 11. The non-transitorycomputer-readable storage medium of claim 7, wherein determining the atleast two consensus units, comprises: determining that the number isless than or equal to the threshold, and selecting each of the at leasttwo consensus units based on the determined load capacity of theconsensus unit.
 12. The non-transitory computer-readable storage mediumof claim 7, wherein the multiple transaction requests comprise a charitydonation transaction or a crowdfunding transaction with no upper limitor quota.
 13. A system, comprising: a processor; and a non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by the processor, cause the processor to perform a methodcomprising: acquiring transaction data from multiple transactionrequests with no sequence requirements, the acquired transaction datacomprising first transaction data of a first transaction request of thetransaction requests and second transaction data of a second transactionrequest of the transaction requests, wherein each of the transactionrequests with no sequence requirements does not require processingaccording to a time factor of when the transaction request was sent by asender relative to other transaction requests acquired for consensusprocessing; determining a quantity of resources required to process theacquired transaction data; determining a number of consensus units in ablockchain network that have a quantity of idle resources that is noless than the quantity of resources required to process the acquiredtransaction data; determining, based on the number, at least twoconsensus units from the consensus units by: if the number is greaterthan a threshold, polling the consensus units to determine which of theconsensus units is in need of a consensus transaction; and if the numberis less than or equal to the threshold, determining which of theconsensus units has a load capacity below a set condition; anddistributing the first transaction data to a first consensus unit of theat least two consensus units and the second transaction data to a secondconsensus unit of the at least two consensus units without consideringthe time factor, causing the at least two consensus units to performconsensus processing on the first transaction data and the secondtransaction data without considering the time factor.
 14. The system ofclaim 13 wherein after the first transaction data and the secondtransaction data are distributed, the first consensus unit and thesecond consensus unit concurrently perform consensus processing on thefirst transaction data and the second transaction data, respectively.